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            <td width="10%" class="headerItem">Current view:</td>
            <td width="35%" class="headerValue"><a href="../../../index.html">top level</a> - <a href="index.html">include/caffe/layers</a> - swish_layer.hpp<span style="font-size: 80%;"> (source / <a href="swish_layer.hpp.func-sort-c.html">functions</a>)</span></td>
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            <td class="headerItem">Test:</td>
            <td class="headerValue">code analysis</td>
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            <td class="headerItem">Lines:</td>
            <td class="headerCovTableEntry">0</td>
            <td class="headerCovTableEntry">4</td>
            <td class="headerCovTableEntryLo">0.0 %</td>
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            <td class="headerItem">Date:</td>
            <td class="headerValue">2020-09-11 22:50:33</td>
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            <td class="headerItem">Functions:</td>
            <td class="headerCovTableEntry">0</td>
            <td class="headerCovTableEntry">8</td>
            <td class="headerCovTableEntryLo">0.0 %</td>
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            <td class="headerValueLeg">            Lines:
            <span class="coverLegendCov">hit</span>
            <span class="coverLegendNoCov">not hit</span>
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<pre class="sourceHeading">          Line data    Source code</pre>
<pre class="source">
<a name="1"><span class="lineNum">       1 </span>            : #ifndef CAFFE_SWISH_LAYER_HPP_</a>
<span class="lineNum">       2 </span>            : #define CAFFE_SWISH_LAYER_HPP_
<span class="lineNum">       3 </span>            : 
<span class="lineNum">       4 </span>            : #include &lt;vector&gt;
<span class="lineNum">       5 </span>            : 
<span class="lineNum">       6 </span>            : #include &quot;caffe/blob.hpp&quot;
<span class="lineNum">       7 </span>            : #include &quot;caffe/layer.hpp&quot;
<span class="lineNum">       8 </span>            : #include &quot;caffe/proto/caffe.pb.h&quot;
<span class="lineNum">       9 </span>            : 
<span class="lineNum">      10 </span>            : #include &quot;caffe/layers/neuron_layer.hpp&quot;
<span class="lineNum">      11 </span>            : #include &quot;caffe/layers/sigmoid_layer.hpp&quot;
<span class="lineNum">      12 </span>            : 
<span class="lineNum">      13 </span>            : namespace caffe {
<span class="lineNum">      14 </span>            : 
<span class="lineNum">      15 </span>            : /**
<span class="lineNum">      16 </span>            :  * @brief Swish non-linearity @f$ y = x \sigma (\beta x) @f$.
<span class="lineNum">      17 </span>            :  *        A novel activation function that tends to work better than ReLU [1].
<span class="lineNum">      18 </span>            :  *
<span class="lineNum">      19 </span>            :  * [1] Prajit Ramachandran, Barret Zoph, Quoc V. Le. &quot;Searching for
<span class="lineNum">      20 </span>            :  *     Activation Functions&quot;. arXiv preprint arXiv:1710.05941v2 (2017).
<a name="21"><span class="lineNum">      21 </span>            :  */</a>
<span class="lineNum">      22 </span>            : template &lt;typename Dtype&gt;
<span class="lineNum">      23 </span><span class="lineNoCov">          0 : class SwishLayer : public NeuronLayer&lt;Dtype&gt; {</span>
<span class="lineNum">      24 </span>            :  public:
<span class="lineNum">      25 </span>            :   /**
<span class="lineNum">      26 </span>            :    * @param param provides SwishParameter swish_param,
<span class="lineNum">      27 </span>            :    *     with SwishLayer options:
<span class="lineNum">      28 </span>            :    *   - beta (\b optional, default 1).
<span class="lineNum">      29 </span>            :    *     the value @f$ \beta @f$ in the @f$ y = x \sigma (\beta x) @f$.
<span class="lineNum">      30 </span>            :    */
<span class="lineNum">      31 </span><span class="lineNoCov">          0 :   explicit SwishLayer(const LayerParameter&amp; param)</span>
<span class="lineNum">      32 </span>            :       : NeuronLayer&lt;Dtype&gt;(param),
<span class="lineNum">      33 </span>            :         sigmoid_layer_(new SigmoidLayer&lt;Dtype&gt;(param)),
<span class="lineNum">      34 </span>            :         sigmoid_input_(new Blob&lt;Dtype&gt;()),
<span class="lineNum">      35 </span><span class="lineNoCov">          0 :         sigmoid_output_(new Blob&lt;Dtype&gt;()) {}</span>
<span class="lineNum">      36 </span>            :   virtual void LayerSetUp(const vector&lt;Blob&lt;Dtype&gt;*&gt;&amp; bottom,
<span class="lineNum">      37 </span>            :       const vector&lt;Blob&lt;Dtype&gt;*&gt;&amp; top);
<span class="lineNum">      38 </span>            :   virtual void Reshape(const vector&lt;Blob&lt;Dtype&gt;*&gt;&amp; bottom,
<a name="39"><span class="lineNum">      39 </span>            :       const vector&lt;Blob&lt;Dtype&gt;*&gt;&amp; top);</a>
<span class="lineNum">      40 </span>            : 
<span class="lineNum">      41 </span><span class="lineNoCov">          0 :   virtual inline const char* type() const { return &quot;Swish&quot;; }</span>
<span class="lineNum">      42 </span>            : 
<span class="lineNum">      43 </span>            :  protected:
<span class="lineNum">      44 </span>            :   /**
<span class="lineNum">      45 </span>            :    * @param bottom input Blob vector (length 1)
<span class="lineNum">      46 </span>            :    *   -# @f$ (N \times C \times H \times W) @f$
<span class="lineNum">      47 </span>            :    *      the inputs @f$ x @f$
<span class="lineNum">      48 </span>            :    * @param top output Blob vector (length 1)
<span class="lineNum">      49 </span>            :    *   -# @f$ (N \times C \times H \times W) @f$
<span class="lineNum">      50 </span>            :    *      the computed outputs @f$
<span class="lineNum">      51 </span>            :    *        y = x \sigma (\beta x)
<span class="lineNum">      52 </span>            :    *      @f$.
<span class="lineNum">      53 </span>            :    */
<span class="lineNum">      54 </span>            :   virtual void Forward_cpu(const vector&lt;Blob&lt;Dtype&gt;*&gt;&amp; bottom,
<span class="lineNum">      55 </span>            :       const vector&lt;Blob&lt;Dtype&gt;*&gt;&amp; top);
<span class="lineNum">      56 </span>            :   virtual void Forward_gpu(const vector&lt;Blob&lt;Dtype&gt;*&gt;&amp; bottom,
<span class="lineNum">      57 </span>            :       const vector&lt;Blob&lt;Dtype&gt;*&gt;&amp; top);
<span class="lineNum">      58 </span>            : 
<span class="lineNum">      59 </span>            :   /**
<span class="lineNum">      60 </span>            :    * @brief Computes the error gradient w.r.t. the sigmoid inputs.
<span class="lineNum">      61 </span>            :    *
<span class="lineNum">      62 </span>            :    * @param top output Blob vector (length 1), providing the error gradient with
<span class="lineNum">      63 </span>            :    *      respect to the outputs
<span class="lineNum">      64 </span>            :    *   -# @f$ (N \times C \times H \times W) @f$
<span class="lineNum">      65 </span>            :    *      containing error gradients @f$ \frac{\partial E}{\partial y} @f$
<span class="lineNum">      66 </span>            :    *      with respect to computed outputs @f$ y @f$
<span class="lineNum">      67 </span>            :    * @param propagate_down see Layer::Backward.
<span class="lineNum">      68 </span>            :    * @param bottom input Blob vector (length 1)
<span class="lineNum">      69 </span>            :    *   -# @f$ (N \times C \times H \times W) @f$
<span class="lineNum">      70 </span>            :    *      the inputs @f$ x @f$; Backward fills their diff with
<span class="lineNum">      71 </span>            :    *      gradients @f$
<span class="lineNum">      72 </span>            :    *        \frac{\partial E}{\partial x}
<span class="lineNum">      73 </span>            :    *            = \frac{\partial E}{\partial y}(\beta y +
<span class="lineNum">      74 </span>            :    *              \sigma (\beta x)(1 - \beta y))
<span class="lineNum">      75 </span>            :    *      @f$ if propagate_down[0]
<span class="lineNum">      76 </span>            :    */
<span class="lineNum">      77 </span>            :   virtual void Backward_cpu(const vector&lt;Blob&lt;Dtype&gt;*&gt;&amp; top,
<span class="lineNum">      78 </span>            :       const vector&lt;bool&gt;&amp; propagate_down, const vector&lt;Blob&lt;Dtype&gt;*&gt;&amp; bottom);
<span class="lineNum">      79 </span>            :   virtual void Backward_gpu(const vector&lt;Blob&lt;Dtype&gt;*&gt;&amp; top,
<span class="lineNum">      80 </span>            :       const vector&lt;bool&gt;&amp; propagate_down, const vector&lt;Blob&lt;Dtype&gt;*&gt;&amp; bottom);
<span class="lineNum">      81 </span>            : 
<span class="lineNum">      82 </span>            :   /// The internal SigmoidLayer
<span class="lineNum">      83 </span>            :   shared_ptr&lt;SigmoidLayer&lt;Dtype&gt; &gt; sigmoid_layer_;
<span class="lineNum">      84 </span>            :   /// sigmoid_input_ stores the input of the SigmoidLayer.
<span class="lineNum">      85 </span>            :   shared_ptr&lt;Blob&lt;Dtype&gt; &gt; sigmoid_input_;
<span class="lineNum">      86 </span>            :   /// sigmoid_output_ stores the output of the SigmoidLayer.
<span class="lineNum">      87 </span>            :   shared_ptr&lt;Blob&lt;Dtype&gt; &gt; sigmoid_output_;
<span class="lineNum">      88 </span>            :   /// bottom vector holder to call the underlying SigmoidLayer::Forward
<span class="lineNum">      89 </span>            :   vector&lt;Blob&lt;Dtype&gt;*&gt; sigmoid_bottom_vec_;
<span class="lineNum">      90 </span>            :   /// top vector holder to call the underlying SigmoidLayer::Forward
<span class="lineNum">      91 </span>            :   vector&lt;Blob&lt;Dtype&gt;*&gt; sigmoid_top_vec_;
<span class="lineNum">      92 </span>            : };
<span class="lineNum">      93 </span>            : 
<span class="lineNum">      94 </span>            : }  // namespace caffe
<span class="lineNum">      95 </span>            : 
<span class="lineNum">      96 </span>            : #endif  // CAFFE_SWISH_LAYER_HPP_
</pre>
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